My second experiment with data analysis deals with analyzing objects in coordinate systems (much like GIS data). I wrote a Grasshopper script that reads coordinate data from Excel and visualizes/analyzes that data in Rhino/Grasshopper.

Below, I’ve attached an image of the script in action. The Excel file with each circle’s coordinates and radius is in the top left and the Grasshopper output is in the bottom left. Grasshopper shades the circles blue if they are located relatively close to other circles and red if they are relatively far from other circles.

Since the script requires a running instance of Excel, I can manipulate the Excel data in real-time and watch the Rhino/Grasshopper visualization compute the changes. Below, I have changed the Excel data input.

This script could be applied to any GIS data analysis, where the circles might be coordinates of parks and users are computing the proximity of parks to other systems.

My hope for a final research experiment is to combine the lessons of these two experiments: flow dynamics/visualization (Research Update One) and coordinate analysis/visualization (Research Update Two) to produce systems dynamics in real GIS data.

A while ago, I scripted a “proof of concept” in Grasshopper. The script shows that Grasshopper (working in parallel with Excel) can calculate, visualize, and record system flows and dynamics.

I’ve scripted Meadows’ simple example of a bathtub with one stock, and two flows: (input and output). In the screenshot below, you can see my “Grasshopped” version of the same system in the Grasshopper interface, which allows me to change the input and output flows with sliders. The script runs on a timer where each step represents a consecutive instance of the system. The script writes data from each step in Excel (Column A is step number, B is Input Flow, C is Output Flow, D is Stock). Grasshopper simultaneously reads the Stock from the previous step to compute the next Stock.

At the same time, Grasshopper visualizes the flow in Rhino as a time line. The circle’s size represents the stock, while its color represents the flow (Red is high input, Blue is high output).

My hope is that this data workflow can lead to dynamic mapping of flows with real GIS data. I am not sure if I will actually get to true GIS data by the end of the semester. I may produce my own coordinate system in Excel.

We worked quite a bit on the representation of haptic space. In our diagrams, color represents perceived heat: gradients of blue and red represent heat flows. Otherwise, physical space in our diagrams is greyscale.

As a waterman in the Bay Game, I had only two decisions each round: dredge the bay or pot the bay. After each round, however, I could scrutinize an abundance of useless spreadsheets on my income, my investments, and my “catch” (there was little data on Bay health). Additionally, I could evaluate my experienced on three “life metrics” (more metrics than possible actions!). Ultimately, there was far more effect than cause in the Bay Game, with no real way for a waterman to intervene.

Moreover, my two actions were largely determined by the water regulator, who was responsible for limiting the percentage of the season I was able to dredge/pot, thus determining my income. Additionally, there was no reward—or punishment—to incentivize a reduction in consumption. The only system in place to evaluate overconsumption was the economy. Yet, since my actions were straight-jacketed by regulation, it was difficult to comprehend how my individual actions influenced the bay economy and health.

Perhaps this feeling of helplessness is a good way to interpret our current condition: people’s actions are so limited by regulation and economy that recourse is unimaginable. This is the Dilemma of the Waterman.

The dilemma of the waterman illustrates two of Meadows’ main points: the tragedy of the commons, and that providing information can engender paradigm shifts. Since the Bay Game did not provide an estimate of the crab population, my approximation was driven by the revenue of my catch (the economy). Since I did not have a complete understanding of how crab prices effected the larger economy, my only response was to dredge/fish as much as possible by regulation with little regard for the “stock” of crab. I had plenty of information but it was the wrong information (useless information on my income). What I needed to perform as a thoughtful waterman was actual data on the crab population. The delay of the Bay Game only decreased my ability to understand cause and effect.

Over the weekend, I scripted an optimized canopy for our bus stop project. Our site is located near Alderman Library/Special Collections with a tree that blocks southern exposure (see previous post). We have decided to stick with the site, as it poses challenges of solar gain and heating during the winter.

A primary function of our bus stop is to provide an underground space for commuters to shower. We will capture water, heat, and energy for our showers with a large bus stop canopy (heated water will be stored in cisterns). Since peak commuter times occur in the morning and evening, the canopy is optimized for solar gain at low altitudes from east and west—when the tree is not blocking solar exposure (the “peak azimuth” is represented in blue on my “solar optimization” diagrams below).

I chose a simple saddle form to optimize solar gain in the mornings and evenings (one half of the canopy is optimized for morning, and one half is optimized for evenings). Due to its curvature, a simple saddle will perform much better than a flat surface (a flat surface can only be optimized for southern exposure, which is blocked by a tree anyway). To optimize the surface throughout the year, I scripted a Grasshoppper component that compares the four “control points” of a simple-saddle shape to the sun’s azimuth and altitude for mornings and evenings throughout the year (see the diagrams of “solar optimization” below).

Another advantage of a simple-saddle is constructability: it’s edges are all straight lines. Our canopy would be a rigid (about 1′ thick) panelized system comprised of three layers: a top layer of thin film photovoltaics (for energy capture), a middle-layer of thin tubing (we would pump water through the tubing and heat it by solar gain), and a bottom-layer of a reflective surface (which would reflect the sun back up into the tubing and solarvoltaics, maximizing our gain). The simple saddle is also an optimized shape for collecting rain water. We would collect water with cisterns at the canopy’s lower edges.